Summary of Forecasting with An N-dimensional Langevin Equation and a Neural-ordinary Differential Equation, by Antonio Malpica-morales et al.
Forecasting with an N-dimensional Langevin Equation and a Neural-Ordinary Differential Equation
by Antonio Malpica-Morales, Miguel A. Duran-Olivencia, Serafim Kalliadasis
First submitted to arxiv on: 12 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Dynamical Systems (math.DS); Data Analysis, Statistics and Probability (physics.data-an); Methodology (stat.ME)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper presents a novel approach for predicting day-ahead electricity prices in competitive markets. While existing methods focus on stationary forecasting techniques, this research addresses the scarcity of non-stationary methods that can capture complex market fluctuations. The proposed framework combines a Langevin equation (LE) and a neural-ordinary differential equation (NODE) to model and forecast non-stationary time series. The LE is effective for fine-grained details in stationary regimes but falls short in capturing non-stationary conditions. To overcome this limitation, the NODE learns and predicts the difference between actual prices and simulated trajectories generated by the LE, reconstructing non-stationary components that the LE cannot capture. The framework’s effectiveness is demonstrated using the Spanish electricity day-ahead market as a case study, outperforming basic naive methods in different non-stationary scenarios. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps predict electricity prices better. It uses special math to figure out what makes electricity prices change over time. Right now, most prediction models just look at how things are today and yesterday, but this research looks at how things have changed over time too. The new method combines two types of calculations: one that works well for steady patterns and another that’s good at finding changes. This helps make the predictions more accurate. The researchers tested their idea using electricity prices in Spain and found it worked better than other simple methods. |
Keywords
» Artificial intelligence » Time series
